Dictionary-based Framework for Interpretable and Consistent Object Parsing

TL;DR

CoCal introduces a dictionary-based, interpretable object parsing framework that leverages contrastive learning and logical constraints to improve segmentation accuracy and consistency, achieving state-of-the-art results on key benchmarks.

Contribution

The paper proposes CoCal, a novel hierarchical dictionary-based mask transformer with contrastive components and logical constraints for improved object parsing.

Findings

Achieves state-of-the-art performance on PartImageNet and Pascal-Part-108.

Outperforms previous methods by 2.08% and 0.70% in part mIoU.

Enhances object-level segmentation metrics.

Abstract

In this work, we present CoCal, an interpretable and consistent object parsing framework based on dictionary-based mask transformer. Designed around Contrastive Components and Logical Constraints, CoCal rethinks existing cluster-based mask transformer architectures used in segmentation; Specifically, CoCal utilizes a set of dictionary components, with each component being explicitly linked to a specific semantic class. To advance this concept, CoCal introduces a hierarchical formulation of dictionary components that aligns with the semantic hierarchy. This is achieved through the integration of both within-level contrastive components and cross-level logical constraints. Concretely, CoCal employs a component-wise contrastive algorithm at each semantic level, enabling the contrasting of dictionary components within the same class against those from different classes. Furthermore, CoCal addresses logical concerns by ensuring that the dictionary component representing a particular part is closer to its corresponding object component than to those of other objects through a cross-level contrastive learning objective. To further enhance our logical relation modeling, we implement a post-processing function inspired by the principle that a pixel assigned to a part should also be assigned to its corresponding object. With these innovations, CoCal establishes a new state-of-the-art performance on both PartImageNet and Pascal-Part-108, outperforming previous methods by a significant margin of 2.08% and 0.70% in part mIoU, respectively. Moreover, CoCal exhibits notable enhancements in object-level metrics across these benchmarks, highlighting its capacity to not only refine parsing at a finer level but also elevate the overall quality of object segmentation.